CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates

TL;DR

CFL-SparseMed introduces a communication-efficient federated learning method for medical imaging that uses Top-k sparsification to reduce data transfer, effectively handling data heterogeneity while maintaining accuracy.

Contribution

It presents a novel FL approach combining Top-k sparsification with medical imaging, addressing communication costs and data heterogeneity simultaneously.

Findings

Reduces communication overhead significantly.

Maintains high diagnostic accuracy in non-IID data settings.

Enhances privacy preservation in federated learning.

Abstract

Secure and reliable medical image classification is crucial for effective patient treatment, but centralized models face challenges due to data and privacy concerns. Federated Learning (FL) enables privacy-preserving collaborations but struggles with heterogeneous, non-IID data and high communication costs, especially in large networks. We propose \textbf{CFL-SparseMed}, an FL approach that uses Top-k Sparsification to reduce communication overhead by transmitting only the top k gradients. This unified solution effectively addresses data heterogeneity while maintaining model accuracy. It enhances FL efficiency, preserves privacy, and improves diagnostic accuracy and patient care in non-IID medical imaging settings. The reproducibility source code is available on \href{https://github.com/Aniket2241/APK_contruct}{Github}.